Work

Multi-Objective Optimization for Nano-Silica-Modified Concrete Based on Explainable Machine Learning

Year: 2025

Type: article

Abstract: Nano-silica modified concrete (NSC) has been widely applied in engineering practice. However, conventional manual mix proportion design is both time-consuming and costly. In this study, four machine learning models—XGBoost, CatBoost, Random Forest, and AdaBoost—were trained to predict the compressive strength of NSC. Based on the best-performing model, the NSGA-II algorithm was employed to develop a multi-objective optimization framework, considering compressive strength, cost, and carbon emissions as objectives. The results indicated that XGBoost achieved the highest accuracy, with R2 = 0.99 and RMSE = 1.80 MPa. Feature importance analysis further revealed that nano-silica content was strongly correlated with strength (0.82) and cost (0.85). Using NSGA-II, a set of Pareto-optimal solutions was generated. The NSGA-II algorithm produced Pareto-optimal solutions, highlighting the trade-offs among the three objectives. This integrated approach effectively reduces experimental workload and provides a valuable reference for sustainable NSC mix proportion design.

Source: Nanomaterials

DOI: 10.3390/nano15181423

Authors: Yue Gu, Ruyan Fan, Yikun Li, Jiaqiang Zhao, Zijian Song, Hongqiang Chu

Institution: Hohai University

Language: English

Topic: Concrete and Cement Materials Research

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,896